This invention relates to an apparatus and method for recognizing input patterns such as voice patterns and character patterns. Pattern recognition is gaining more acceptance as a fundamental technique for inputting information into computer systems. One method called the similarity method or pattern matching method is a well-known pattern recognition process and is widely utilized in the field of character recognition. Several different similarity methods are known, including the simple similarity method, multiple similarity method and mixed similarity method.
The simple similarity method utilizes a single separate reference pattern, for each category, represented by a vector which is stored in a dictionary memory. A single reference pattern corresponding to each designated category represents, for example, a certain character or voice pattern to be identified. That is, one category can consist of the letter (A), another category can consist of lower case letter (a). In voice recognition, separate categories can consist of the respective sounds for pronouncing each of the vowels (a, e, i, o and u). These reference patterns are then compared with the vector representations of the patterns to be identified (i.e., input patterns) to determine its numerical value of similarity. A high value of similarity indicates that the input pattern is identical or nearly identical to the reference pattern. In particular, the simple similarity method can be performed as follows. First, signals representing the input pattern are sampled and these discrete sampled values are stored as vector components of the input signal. This input vector is then compared with the vectors representing each category. A numerical value of similarity is then calculated for each category which indicates the degree of similarity between the input pattern and the reference pattern for each category. Second, the maximum value of similarity is determined from all the calculated values; this value thus identifies the category to which the input patterns belong.
This simple similarity method has an advantage in that the design of the dictionary of reference patterns can be easily automated, and is not greatly affected by such local noise as stain or scratches in the patterns. It is liable to be affected adversely, however, by such overall changes in the patterns which occur in handwritten letters or voice patterns. That is due to the wide variation in handwriting and voice patterns or pronounications, more deformations in the input pattern can occur. Thus, it is impractical to represent each category by a single reference pattern.
Consequently, other methods have been devised to recognize the input pattern in view of such wide deformations. One such method is the multiple similarity method as disclosed in U.S. Pat. No. 3,688,267 and the mixed similarity method as disclosed in U.S. Pat. No. 3,906,446.
According to the multiple similarity method, a plurality of reference pattern vectors are created for each category. The multiple similarity for a certain category is defined as the sum of the square root of the values of simple similarity between the input pattern and every reference pattern in the same category. As in the case of simple similarity discussed above, recognition is carried out as follows. First, signals representing the input pattern are sampled and these discrete sampled values are stored as vector components of the input signal. This input vector is then compared with each reference pattern vector in the same category. A numerical value of similarity is then calculated for each comparison; the square root of these values are then summed to provide a multiple similarity value for each category. Second, the maximum value of similarity is detected from all calculated values; this value thus identifies the category to which the input pattern belongs.
In the case of mixed similarity, the procedures discussed above for multiple similarity are employed. In addition, the similarity values for mutually similar reference patterns are identified and subtracted to provide even more accurate identification.
The above-described multiple similarity and mixed similarity methods are useful to recognize patterns capable of having numerous variations or overall deformations. However, the conventional systems employing such methods require storage of numerous reference patterns to provide sufficient data to accurately identify and recognize various input patterns. In fact, it is very costly and time consuming to compile the necessary data. Not only is an unduly large memory capacity needed, but excessive computer time is required to calculate the numerous matrix calculations needed to analyze and compare the various stored reference pattern data and input patterns. Consequently, preparation and computation of reference patterns stored in a computer memory for achieving complete and accurate recognition of patterns subject to various deformations have been impractical. As a result, many systems have been developed with a limited reference pattern storage to avoid the cost and incident problems discussed above; consequently, misrecognition has frequently occured when input patterns subject to various deformations have been applied. The industry, therefore, has required a system which can easily be adapted and tailored for special and individual needs without developing an unduly large common memory of reference patterns.